Identification of patterns in sound data, also known as pattern matching, may be utilized to support a wide variety of different usage scenarios. This may include audio source separation, which may involve identification of sound data that corresponds to different sound sources. For example, audio source separation may be performed to remove noise from a recording, separate different speakers in a dialog, and so on. In another example, pattern matching may be used to support word spotting and audio retrieval, such as a part of voice recognition (e.g., a virtual phone menu) by identifying particular keywords in the sound data, to locate sound data having desired keywords or other sounds, and so on.
Conventional techniques that were utilized to identify patterns in sound data, however, typically relied on a matrix representation of the sound data. This representation could be resource intensive to analyze, even when confronted with sparse sound data in which most of the frequency energies are close to zero. Consequently, such representations may be ill suited to real time scenarios and result in needless consumption of computational resources.